DeepVO [1]} utilizes a combination of convolutional neural network (ConvNet) and recurrent neural network (RNN) to enable end-to-end learning of visual odometry. The framework of DeepVO becomes a typical choice in realizing supervised learning of VO, due to its specialization in end-to-end learning.
Figure REF  (a) shows the architecture of this RNN+ConvNet based VO system, which extracts visual features from pairs of images via a ConvNet, and passes features through RNNs to model the temporal correlation of features. Its ConvNet encoder is based on a FlowNet structure to extract visual features suitable for optical flow and self-motion estimation. Using a FlowNet based encoder can be regarded as introducing the prior knowledge of optical flow into the learning process, and potentially prevents DeepVO from being overfitted to the training datasets. The reccurent model summarizes the history information into its hidden states, so that the output is inferred from both past experience and current ConvNet features from sensor observation.
It is trained on large-scale datasets with groundtruthed camera poses as labels. To recover the optimal parameters \(\mathbf {\theta }^{*}\)  of framework, the optimization target is to minimize the Mean Square Error (MSE) of the estimated translations \(\mathbf {\hat{p}} \in \mathbb {R}^3\)  and euler angle based rotations \(\hat{\mathbf {\varphi }} \in \mathbb {R}^3\) :
\(\mathbf {\theta }^{*} = \operatornamewithlimits{arg\,min}_{\mathbf {\theta }} \frac{1}{N} \displaystyle \sum _{i=1}^{N} \displaystyle \sum _{t=1}^{T} \Vert  \hat{\mathbf {p}}_t - \mathbf {p}_t \Vert _2^2 + \Vert  \hat{\mathbf {\varphi }}_t - \mathbf {\varphi }_t \Vert _2^2 ,\) 
